\(\bullet \)  Misspecified Bandits.
Linear bandit algorithms are designed under the assumption that the expected rewards are perfectly linear functions of the actions; misspecified bandits relax this assumption by considering perturbations of the linear model measured by the amount of deviation in the expected rewards (we call the case \(\epsilon \) -misspecification if the deviation is upper bounded by \(\epsilon \) ).
The non-linearity in the model enables to better capture real-world environments and is of high interest in the literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
